Why deployment delivers value in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the model's prediction on new data.

ML Python

prediction = model.[1](new_data)
print(prediction)

Drag options to blanks, or click blank then click option'

Atransform

Bpredict

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'predict' to get predictions.

The predict method is used to get the model's output on new data.

2fill in blank

medium

Complete the code to save the trained model for deployment.

ML Python

import joblib
joblib.[1](model, 'model.pkl')

Drag options to blanks, or click blank then click option'

Aload

Bsave

Cdump

Dexport

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load' instead of 'dump' to save the model.

The dump function saves the model to a file for later use.

3fill in blank

hard

Fix the error in the code to deploy a model as a web service.

ML Python

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.[1](data['input'])
    return jsonify({'prediction': prediction.tolist()})

Drag options to blanks, or click blank then click option'

Afit

Bevaluate

Ctrain

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' or 'train' instead of 'predict' in deployment code.

Use predict to get the model's output on input data in deployment.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters features with importance above 0.1.

ML Python

important_features = {feature: importance for feature, importance in model.feature_importances_.items() if importance [1] 0.1 and feature [2] 'bias'}

Drag options to blanks, or click blank then click option'

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for importance check.

Using '==' instead of '!=' to exclude 'bias'.

We want features with importance greater than 0.1 and exclude the 'bias' feature.

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions for inputs with confidence above 0.8.

ML Python

high_confidence_preds = [1]: [2] for [3] in inputs if model.predict_proba([[3]])[0][1] > 0.8}

Drag options to blanks, or click blank then click option'

Ainput

Bmodel.predict([input])[0]

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using predict_proba output directly as prediction.

We use 'input' as the key and the model's prediction as the value for each input in inputs.

Practice

1. Why is deploying a machine learning model important for businesses?

easy

A. It reduces the size of the dataset.

B. It trains the model faster.

C. It changes the model's algorithm.

D. It allows the model to provide real-time predictions to users.

Solution

1. Step 1: Understand deployment purpose

   Deployment makes the model available to users or systems to use its predictions.

2. Step 2: Connect deployment to business value

   Real-time predictions help businesses make decisions quickly and automatically.

3. Final Answer:

   It allows the model to provide real-time predictions to users. -> Option D

4. Quick Check:

   Deployment = real-time predictions [OK]

Hint: Deployment means making model predictions available [OK]

Common Mistakes:

• Thinking deployment trains the model
• Confusing deployment with data size reduction
• Believing deployment changes the algorithm

2. Which of the following is the correct way to describe model deployment?

easy

A. Saving the model to a file on your computer.

B. Making the model accessible to applications or users.

C. Training the model with more data.

D. Deleting the model after training.

Solution

1. Step 1: Define deployment

   Deployment means making the model usable by others, not just saving it locally.

2. Step 2: Compare options

   Only Making the model accessible to applications or users. describes making the model accessible to applications or users.

3. Final Answer:

   Making the model accessible to applications or users. -> Option B

4. Quick Check:

   Deployment = accessibility [OK]

Hint: Deployment means sharing model for use, not just saving [OK]

Common Mistakes:

• Confusing deployment with saving files
• Mixing deployment with training
• Thinking deployment means deleting the model

3. Consider this code snippet for deploying a model API:

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    prediction = model.predict([data['features']])
    return jsonify({'prediction': prediction[0]})

if __name__ == '__main__':
    app.run()

What is the main value this deployment provides?

medium

A. It allows users to get predictions by sending data to the API.

B. It trains the model faster using Flask.

C. It stores the model in a database.

D. It visualizes the model's training process.

Solution

1. Step 1: Analyze the Flask API code

   The code creates a web service that accepts data and returns predictions.

2. Step 2: Identify deployment value

   This lets users send data and receive model predictions automatically.

3. Final Answer:

   It allows users to get predictions by sending data to the API. -> Option A

4. Quick Check:

   API deployment = user predictions [OK]

Hint: Deployment exposes model predictions via API [OK]

Common Mistakes:

• Thinking Flask trains the model
• Confusing deployment with data storage
• Assuming code visualizes training

4. A developer deployed a model but users report no predictions are returned. The deployment code is:

@app.route('/predict', methods=['GET'])
def predict():
    data = request.json
    prediction = model.predict([data['features']])
    return jsonify({'prediction': prediction[0]})

What is the likely error?

medium

A. Flask app is not imported.

B. The model.predict function is incorrect.

C. Using GET method instead of POST to receive JSON data.

D. The route URL is missing a slash.

Solution

1. Step 1: Check HTTP method for JSON data

   GET requests usually do not have a JSON body; POST is needed to send JSON.

2. Step 2: Identify mismatch in method

   The code uses GET but expects JSON data, causing no data received.

3. Final Answer:

   Using GET method instead of POST to receive JSON data. -> Option C

4. Quick Check:

   JSON data requires POST method [OK]

Hint: Use POST to send JSON data in API calls [OK]

Common Mistakes:

• Assuming GET can carry JSON body
• Blaming model.predict without checking method
• Ignoring HTTP method importance

5. A company wants to deploy a machine learning model to improve customer support by automatically answering common questions. Which deployment approach delivers the most value?

hard

A. Deploy the model as a real-time chatbot API accessible 24/7.

B. Deploy the model as a batch job that runs once a week.

C. Deploy the model only on the data scientist's laptop.

D. Deploy the model without monitoring or updates.

Solution

1. Step 1: Understand business need for customer support

   Customers expect quick answers anytime, so real-time access is key.

2. Step 2: Evaluate deployment options

   Batch jobs delay responses; laptop deployment limits access; no monitoring risks failures.

3. Step 3: Choose best deployment for value

   A real-time chatbot API available 24/7 provides immediate, continuous support.

4. Final Answer:

   Deploy the model as a real-time chatbot API accessible 24/7. -> Option A

5. Quick Check:

   Real-time API = maximum customer support value [OK]

Hint: Real-time, accessible deployment maximizes user value [OK]

Common Mistakes:

• Choosing batch jobs for real-time needs
• Limiting deployment to one machine
• Ignoring monitoring and updates